A Conformer Based Automated Speech Recognition for Armenian Language

Abstract

The article is aimed to represent Armenian automated speech recognition model and its applications in different fields of economy. Because of the lack of Armenian speech corpora, in this article we fine-tuned the voice recognition and text symbol generating parts using a Conformer pre-trained model and a compact Armenian language model. The article focuses the attention of readers on the problem of recognizing human speech and transforming it to a text, especially for non-mainstream languages. The paper is prepared with scientific abstraction and a combined analysis of many recent implementations of the discussed approach. The sources' credibility, relevance and authenticity have been confirmed by their extensive research. As to conclude, though it is pretty challenging to develop ASR model for non-mainstream languages, it was proven that the employment of Conformerbased transformers in conjunction with language models is effective for Armenian speech recognition. It was also proven that the technique employed in this article is applicable for other languages too, with some adjustments.